Abstract
In this paper, I discuss what it will mean for democratic public spheres if more and more content is produced by generative technology based on large language models (LLMs), as represented by OpenAI in the form of Generative Pre-training Transformer (GPT). Although this type of technology can clearly help improve public discourse on a number of parameters, my reading of Deleuze leads me to contend that its heavy reliance on analyses of historical content is a shortcoming. Deleuze has argued that fruitful exchanges require an understanding of how silence helps to constitute what is said. If we cannot work flexibly with silence, what is said loses value. The paper elaborates on this point by discussing how Heidegger’s Fourfold can be read as suggestions for how silence determines what we can say in communicative exchanges. Based on analyses of the technical foundations underpinning AI technology, I argue they tend to reproduce existing structures of silence and that this makes it important to realise that allowing AI to play a greater role in the production of public content still leaves us with the task of challenging existing structures. If we do not face up to that challenge, something will be lost in terms of the ability of the citizen to relate critically and reflexively to the discursive horizons of ongoing public discourse.
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This research does not involve the analysis or generation of any data.
Notes
Notice, that this kind of silence is not identical with the structural kind of silence which is the main focus of this paper. In Deleuze’s text, structural silence is reflected in his analyses of style – see below.
This part of Deleuze’s argument has great similarities with Heidegger’s early analyses of how “Rede” (discourse) deteriorates into “Gerede” (idle talk – Heidegger, 2010, § 34–35; Hansen 2024b, 2024a). It should be noted, however, that just as with Heidegger, though in a different way, Deleuze’s notion of repetition is initially ontological. Deleuze is not as such critical of repetition, however in the context the problem is passive repetition (decoupled from the notion of difference – McMahon, 2011; Colebrook, 2002, p. 5).
The causes for bias in AI-generated texts are certainly multiple. It is also well documented that it is impossible to avoid bias completely – which will also be a point in the following. It thus remains a key assignment for users of the technologies to take this into account (Wellner & Rothman 2020). However, this paper focusses upon the silencing aspect.
According to a published whitepaper, the following axes of bias are considered key: “user invalidation”, “user escalation”, “personal political expression”, “asymmetric coverage”, “political refusal” (OpenAI, 2025a).
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Hansen, E. Analysing Structures of Silence in AI-mediated Public Spheres. Philos. Technol. 39, 175 (2026). https://doi.org/10.1007/s13347-026-01183-5
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DOI: https://doi.org/10.1007/s13347-026-01183-5